This research project addresses the potential mental health impacts produced by the use of capital punishment. In the last 22 years, the state of Texas performed more than three times as many executions as any other state (Death Penalty Information Center 2002; Texas Department of Criminal Justice 2002). However, social scientists have largely failed to consider, let alone assess, the potential effects this policy of wholesale executions may have on the mental health of Texas citizens, especially for groups over-represented among executed prisoners, such as African-Americans and Hispanics, and those from urban areas. As a potential source of psychological or traumatic stress, exposure to the use of violence by the state may affect the emotions and behaviors of citizens. Because executions entail violence, the mental health implications of executions may manifest themselves as a risk factor for subsequent violence among the populace- because executions may "brutalize" society as a whole by devaluing human life, and by legitimizing violence or by mitigating its deviant qualities. Therefore , this research project proposes to assess the effects of executions on homicides, a specific form of violence. While homicide by no means exhausts the realm of violent behaviors, changes in homicides are a good indicator of changes in violence more generally, and homicide data are much more accurate and detailed than any other data on violent behavior. Data are available for both executed prisoners and for homicides for the last 22 years in Texas. These data will enable an assessment of the effect of executions on homicides for the state of Texas over time, and for different race/ethnicity, gender, and geographic subgroups (e.g., urban compared to rural). Because these data are "time-series," statistical analyses are especially challenging. Therefore, this proposed research will employ an "autoregressive integrated moving average" (ARIMA) approach - a relatively new and highly sophisticated analytic strategy that can effectively deal with the problems posed by time series data.